无需训练的奖励引导图像编辑 via 轨迹最优控制
Training-Free Reward-Guided Image Editing via Trajectory Optimal Control
AI总结：
本文提出无需训练的奖励引导图像编辑框架，将编辑过程建模为轨迹最优控制问题，通过迭代更新伴随状态以引导编辑过程，在保持源图像语义的同时提升目标奖励。
摘要：
近期扩散模型和流匹配模型的进步展示了在高保真图像合成中的显著能力。一个显著的研究方向是奖励引导引导，它在推理过程中引导生成过程以符合特定目标。然而，将这种奖励引导方法应用于图像编辑任务，即在保持源图像语义内容的同时增强目标奖励，尚属空白。在本文中，我们提出了一种新的无需训练的奖励引导图像编辑框架。我们将编辑过程建模为轨迹最优控制问题，其中扩散模型的逆过程被视为从源图像出发的可控轨迹，伴随状态被迭代更新以引导编辑过程。通过在不同编辑任务上的广泛实验，我们证明了我们的方法在现有基于反向训练的无训练引导基线之上显著优于，实现了在奖励最大化和源图像保真度之间的更优平衡，而无需奖励黑客。
英文摘要：
Recent advancements in diffusion and flow-matching models have demonstrated remarkable capabilities in high-fidelity image synthesis. A prominent line of research involves reward-guided guidance, which steers the generation process during inference to align with specific objectives. However, leveraging this reward-guided approach to the task of image editing, which requires preserving the semantic content of the source image while enhancing a target reward, is largely unexplored. In this work, we introduce a novel framework for training-free, reward-guided image editing. We formulate the editing process as a trajectory optimal control problem where the reverse process of a diffusion model is treated as a controllable trajectory originating from the source image, and the adjoint states are iteratively updated to steer the editing process. Through extensive experiments across distinct editing tasks, we demonstrate that our approach significantly outperforms existing inversion-based training-free guidance baselines, achieving a superior balance between reward maximization and fidelity to the source image without reward hacking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
